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Contact

Education

PhD., Statistics
Boston University
Expected 2025

MS., Statistics
University of Rhode Island
2017 – 2019

BS., Statistics
University of Sri Jayewardenepura
2011 – 2015

Language Skills

Python
R
Julia
Stan
SQL

Awards and Services

Interests

Main

Manushi Welandawe

As a statistician and data scientist, I focus on building fast, robust methods that bridge the gap between theory and real-world data challenges. My work combines scalable Bayesian inference, stochastic optimization, and practical modeling tools to accelerate scientific discovery and decision-making across different fields. I’m passionate about creating solutions that are both statistically sound and computationally efficient.

Experience

Graduate Research Fellow

Boston University

Boston, MA

Present - 2020

  • Developing novel diagnostics for detecting stationarity in variational and Bayesian inference contexts
  • Developed a robust framework for reliable variational inference with convergence diagnostics and variational approximation assessment

Senior Teaching Fellow

Boston University

Boston, MA

2023

  • Instructor for MA 214: Applied Statistics, providing instruction and hands-on support to students; evaluated and graded final group projects to assess applied statistical analysis skills

NSF-MSG Intern

Argonne National Laboratory

Lemont, IL

2022

  • Investigated theoretical and empirical properties of gradient estimators in zeroth-order/derivative-free stochastic optimization

Graduate Teaching Fellow

Boston University

Boston, MA

2021 - 2019

  • Led 3 discussions each week for graduate level course MA 585 Time series and Forecasting
  • Led 5 discussions each week for MA113 Elementary Statistics
  • Led 4 discussions each week for MA116 Statistics II

Graduate Researcher

University of Rhode Island

Kingston, RI

2019 - 2018

  • Designed a Bayesian mixed-effects zero-inflated beta regression model for longitudinal microbiome data with missing-at-random patterns, validated via simulations and applied to real-world datasets

Graduate Administrative Assistant

University of Rhode Island

Kingston, RI

2019 - 2018

  • Organized and conducted workshops on R, SAS, and SPSS for the University of Rhode Island (URI) faculty, graduate students, and undergraduate community
  • Provided statistical consultation to local researchers on projects utilizing R, SAS, and SPSS

Graduate Teaching Assistant

University of Rhode Island

Kingston, RI

2017

  • Led 5 discussions per week for STA 220 Statistics in Modern Society
  • Graded final semester exams for STA 308 Introductory Statistics

Junior Analyst

Peppercube Consultants (Pvt.) Ltd.

Sri Lanka

2017

  • Conducted statistical analysis for market research to gain insights in existing or newly developed products and services

Teaching Assistant

University of Sri Jayewardenepura

Sri Lanka

2016

  • Led discussions, graded homework, and proctored  final semester exams for STA 122/221 Data Analysis I/II

Publications

A framework for improving the reliability of black-box variational inference

Journal of Machine Learning Research

N/A

2024

  • Authored with Michael Riis Andersen, Aki Vehtari, and Jonathan H. Huggins.

Challenges and opportunities in high dimensional variational inference

Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

N/A

2021

  • Authored with Akash Kumar Dhaka, Alejandro Catalina, Michael R Andersen, Jonathan Huggins, and Aki Vehtari

A Survival Analysis of the Gulf Stream Warm Core Rings

Journal of Geophysical Research: Oceans

N/A

2020

  • Authored with E. Nishchitha S. Silva, Avijit Gangopadhyay, Gavin Fay, Glen Gawarkiewicz, Adrienne M. Silver, Mahmud Monim, and Jenifer Clark

Effects of early life NICU stress on the developing gut microbiome

Developmental Psychobiology

N/A

2019

  • Authored with Amy L. D’Agata, Jing Wu, Samia V. O. Dutra, Bradley Kane, and Maureen W. Groer

Invited Talks

A Framework to Enhance the Reliability and Detect Convergence of Black-box Variational Inference

N/A

N/A

2024

New England Statistics Symposium

Robust, Automated, and Accurate Black-box Variational Inference

N/A

N/A

2022

Bayes Reading Group, Flatiron Institute